In this work, we consider what is the simplest way to obtain goal conditioned control in a dynamic real world setting such as precision table tennis? Can one design effective alternatives to more intricate RL algorithms that perform well in this difficult setup? In pursuit of this question, we consider the necessity of different components in existing goal conditioned learning pipelines, both RL and IL. Surprisingly, we find that the synthesis of two existing techniques in iterative self-supervised imitation learning [1]}, [2]} indeed scales to this setting. For ease of reference, we refer to this best performing approach throughout as GoalsEye, a system for high-precision goal reaching table tennis, trained with goal conditioned behavior cloning plus self-supervised practice (GCBC\(+\) SSP).
